Estimation of terramechanical properties

ABSTRACT

A system for estimating tire parameters for an off-road vehicle in real time, the system including a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to measure a position of the vehicle at a first time, determine, based on the position, motion characteristics of the vehicle, predict, based on the motion characteristics, a position of the vehicle at a second time, measure a position of the vehicle at the second time, and generate a tire parameter associated with the vehicle based on the predicted position and the measured position of the vehicle at the second time.

BACKGROUND

The present disclosure relates generally to the field of vehicle controlsystems, and more particularly to a system and method of determiningvehicle tire parameters for off-road vehicles.

SUMMARY

One implementation of the present disclosure is a system for estimatingtire parameters for an off-road vehicle in real time, the systemincluding a processing circuit including a processor and memory, thememory having instructions stored thereon that, when executed by theprocessor, cause the processing circuit to measure a position of thevehicle at a first time, determine, based on the position, motioncharacteristics of the vehicle, predict, based on the motioncharacteristics, a position of the vehicle at a second time, measure aposition of the vehicle at the second time, and generate a tireparameter associated with the vehicle based on the predicted positionand the measured position of the vehicle at the second time.

In some embodiments, the tire parameter is a cornering stiffness. Insome embodiments, the tire parameter is a tire type. In someembodiments, the tire parameter is generated based on a correctionfactor associated with a difference between the predicted position andthe measured position of the vehicle at the second time. In someembodiments, the correction factor is associated with an amount of tireslip associated with the difference between the predicted position andthe measured position of the vehicle at the second time. In someembodiments, generating the tire parameter includes adjusting thecorrection factor to account for the difference between the predictedposition and the measured position of the vehicle at the second time,wherein the adjusted correction factor is the tire parameter. In someembodiments, the difference between the predicted position and themeasured position of the vehicle at the second time includes two or moreparameters associated with the vehicle position and wherein the methodincludes weighting each of the two or more parameters based on acontribution each of the two or more parameters make to the differencebetween the predicted position and the measured position of the vehicleat the second time. In some embodiments, the vehicle is an agriculturalvehicle. In some embodiments, measuring the position of the vehicle atthe first and second times includes receiving position information froma GPS receiver associated with the vehicle. In some embodiments, thetire parameter is generated further based on vehicle characteristicsassociated with the vehicle. In some embodiments, the processing circuitis further configured to control an operation of the vehicle based onthe tire parameter.

Another implementation of the present disclosure is a method ofestimating tire parameters for an off-road vehicle in real time, themethod including measuring a position of the vehicle at a first time,determining, based on the position, motion characteristics associatedwith the vehicle, predicting, based on the motion characteristics, aposition of the vehicle at a second time, measuring a position of thevehicle at the second time, and generating a tire parameter associatedwith the vehicle based on the predicted position and the measuredposition of the vehicle at the second time.

In some embodiments, the tire parameter is a cornering stiffness. Insome embodiments, the tire parameter is a tire type. In someembodiments, the tire parameter is generated based on a correctionfactor associated with a difference between the predicted position andthe measured position of the vehicle at the second time. In someembodiments, the correction factor is associated with an amount of tireslip associated with the difference between the predicted position andthe measured position of the vehicle at the second time. In someembodiments, generating the tire parameter includes adjusting thecorrection factor to account for the difference between the predictedposition and the measured position of the vehicle at the second time,wherein the adjusted correction factor is the tire parameter. In someembodiments, the difference between the predicted position and themeasured position of the vehicle at the second time includes two or moreparameters associated with the vehicle position and wherein the methodincludes weighting each of the two or more parameters based on acontribution each of the two or more parameters make to the differencebetween the predicted position and the measured position of the vehicleat the second time. In some embodiments, the vehicle is an agriculturalvehicle.

Another implementation of the present disclosure is an agriculturalvehicle having one or more tires and a vehicle control system includinga processor and memory, the memory having instructions stored thereonthat, when executed by the processor, cause the processor to receive aposition measurement associated with the agricultural vehicle at a firsttime, determine, based on the position, motion characteristicsassociated with the agricultural vehicle, generate, based on the motioncharacteristics, a predicted position of the agricultural vehicle at asecond time, measure a position of the agricultural vehicle at thesecond time, and generate a cornering stiffness associated with at leastone of the one or more tires based on a difference between the predictedposition and the measured position of the agricultural vehicle at thesecond time.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects and features of the present disclosure willbecome more apparent to those skilled in the art from the followingdetailed description of the example embodiments with reference to theaccompanying drawings.

FIG. 1 is a block diagram of a vehicle having a vehicle control system,according to an exemplary embodiment.

FIG. 2 is a diagram illustrating a process for estimating a vehicleparameter, according to an exemplary embodiment.

FIG. 3 is a block diagram of a feedback control loop for performing theprocess of FIG. 2 , according to an exemplary embodiment.

FIG. 4A is a block diagram of a control system that may be implementedby the vehicle control system of FIG. 1 , according to an exemplaryembodiment.

FIG. 4B is another block diagram of a control system that may beimplemented by the vehicle control system of FIG. 1 , according to anexemplary embodiment.

FIG. 5 is a flow diagram of a method of determining a vehicle parameter,according to an exemplary embodiment.

DETAILED DESCRIPTION

Referring generally to the FIGURES, described herein are systems andmethods of a vehicle control system. In various embodiments, the vehiclecontrol system monitors a position of a vehicle and determinesparameters associated with the vehicle based on the position of thevehicle. For example, in an off-road context, a vehicle (e.g., atractor, etc.) may determine a slip angle and/or a tire stiffnessassociated with tires of the vehicle based on position informationassociated with the vehicle. As an additional example, in a constructioncontext, a construction vehicle (e.g., a dump truck, an excavator, etc.)may display tire a tire stiffness to an operator. As an additionalexample, in an agricultural context, an agricultural vehicle (e.g., acombine harvester, a hauling vehicle, etc.) may use a slip angle as aninput for a guided navigation system. In various embodiments, thevehicle control system receives inputs from one or more sources. Forexample, the vehicle control system may receive geospatial data from aglobal positioning system (GPS) receiver. In some embodiments, thevehicle control system analyzes the geospatial data to determine one ormore position/orientation parameters (e.g., yaw, yaw rate, velocity,acceleration, heading, pitch, etc.). In various embodiments, the vehiclecontrol system uses the one or more position/orientation parameters topredict a future position of the vehicle. For example, the vehiclecontrol system may determine a predicted future heading and accelerationof the vehicle based on the current heading and acceleration of thevehicle. In various embodiments, the vehicle control system measures adifference between the predicted future position of the vehicle and ameasured position of the vehicle at the future time. For example, thevehicle control system may determine that the vehicle is ten feet to theleft of a predicted future position at the future time. In variousembodiments, the vehicle control system analyzes the difference betweenthe predicted future position of the vehicle and measured position ofthe vehicle at the future time to determine one or more vehicleparameters associated with the vehicle. For example, the vehicle controlsystem may determine a slip angle between tires of the vehicle and theground. In some embodiments, the vehicle control system may use the oneor more vehicle parameters to operate the vehicle. For example, thevehicle control system may utilize the one or more vehicle parameters todetermine control signals (e.g., a steering angle, acceleration, etc.)for a primary mover associated with the vehicle. Additionally oralternatively, the vehicle control system may send the one or morevehicle parameters to other systems. For example, vehicle control systemmay send the one or more vehicle parameters to a human-machine interface(HMI) associated with the vehicle for display to a user. As anadditional example, vehicle control system may display the one or morevehicle parameters to a user in response to the one or more vehicleparameters exceeding a threshold and/or being out of a threshold range.

Hereinafter, example embodiments will be described in more detail withreference to the accompanying drawings. Referring now to FIG. 1 , ablock diagram of a control environment 100 is shown, according to anexemplary embodiment. Control environment 100 is shown to includevehicle 10. In various embodiments, vehicle 10 is an off-road vehicle.For example, vehicle 10 may be an agricultural vehicle such as a haulingvehicle (e.g., a tractor, etc.), a harvesting vehicle (e.g., a combineharvester, etc.), and/or the like. As a further example, vehicle 10 maybe a construction vehicle such as a loader (e.g., a front loader, abackhoe loader, etc.), a dump truck, and/or the like. Additionally oralternatively, vehicle 10 may be or include a number of different typesof vehicles. While the vehicle control system of the present disclosureis described in relation to off-road vehicles, it should be understoodthat the vehicle control system is usable with other vehicles (e.g.,non-off-road vehicles) and that such embodiments are within the scope ofthe present disclosure. As a non-limiting example, in a mixed context(e.g., on-road/off-road context, etc.), vehicle 10 may be a golf cart.As another non-limiting example, in an on-road context, vehicle 10 maybe a winter service vehicle including a snowplow.

Vehicle 10 includes vehicle control system 110, human-machine interface(HMI) 120, primary mover 130, sensor(s) 140, and communication system150. Vehicle control system 110 may determine vehicle parameters asdescribed herein. In various embodiments, vehicle control system 110 isphysically located with vehicle 10. For example, vehicle control system110 may be or include a hardware component installed in vehicle 10.Additionally or alternatively, vehicle control system 110 may be locatedseparately of vehicle 10. For example, vehicle control system 110 may beor include a cloud-processor configured to receive input from vehicle 10and control vehicle 10 remotely.

HMI 120 may facilitate user interaction with vehicle 10 and/or vehiclecontrol system 110. HMI 120 may include elements configured to presentinformation to a user and receive user input. For example, HMI 120 mayinclude a display device (e.g., a graphical display, a touchscreen,etc.), an audio device (e.g., a speaker, etc.), manual controls (e.g.,manual steering control, manual transmission control, manual brakingcontrol, etc.), and/or the like. HMI 120 may include hardware and/orsoftware components. For example, HMI 120 may include a microphoneconfigured to receive user voice input and a software componentconfigured to control vehicle 10 based on the received user voice input.In various embodiments, HMI 120 presents information associated with theoperation of vehicle 10 and/or vehicle control system 110 to a user andfacilitates user control of operating parameters. For example, HMI 120may display operational parameters (e.g., fuel level, seed level,penetration depth of ground engaging tools, guidance swath, etc.) on atouchscreen display and receive user control input via the touchscreendisplay.

Primary mover 130 may generate mechanical energy to operate vehicle 10.For example, primary mover 130 may be or include an internal combustionengine. Additionally or alternatively, primary mover 130 may be orinclude an electric motor. In various embodiments, primary mover 130 iscoupled to a frame of vehicle 10 and configured to provide power to aplurality of tractive elements (e.g. wheels, etc.). In variousembodiments, primary mover 130 utilizes one or more fuels and/or energystorage systems (e.g., rechargeable batteries, etc.). For example,primary mover 130 may utilize diesel, gasoline, propane, natural gas,hydrogen, lithium-ion batteries, nickel-metal hydride batteries,lithium-ion polymer batteries, lead-acid batteries, nickel-cadmiumbatteries, and/or the like.

Sensor(s) 140 may monitor one or more parameters associated with vehicle10. For example, sensor(s) 140 may monitor operation of primary mover130 (e.g., torque, temperature, fuel level, airflow, etc.). Additionallyor alternatively, sensor(s) 140 may monitor an environment of vehicle10. For example, sensor(s) 140 may include cameras to view thesurroundings of vehicle 10 and perform object recognition to facilitateobstacle avoidance. Sensor(s) 140 may include engine sensors,transmission sensors, chassis sensors, safety sensors, driver assistancesensors, passenger comfort sensors, entertainment systems sensors,and/or the like. In various embodiments, sensor(s) 140 monitorgeospatial parameters associated with vehicle 10. For example, sensor(s)140 may include a geolocation sensor (e.g., a GPS receiver, satellitenavigation transceiver, etc.) configured to monitor a position ofvehicle 10 (e.g., provide geolocation and/or time information, etc.).Sensor(s) 140 may measure an absolute position of vehicle 10 (e.g., alocation, etc.), a relative position of vehicle 10 (e.g., adisplacement, a linear travel, a rotational angle, etc.), and/or athree-dimensional position of vehicle 10. In some embodiments, sensor(s)140 receive input from external sources. For example, sensor(s) 140 mayinclude position sensors configured to communicate with one or morebeacons located throughout a farm field to determine a location ofvehicle 10. In various embodiments, sensor(s) 140 are physically locatedwith vehicle 10. For example, sensor(s) 140 may include a chassismounted infra-red sensor configured to measure crop health. Additionallyor alternatively, sensor(s) 140 may be located separately of vehicle 10.For example, sensor(s) 140 may include a nitrogen sensor configured tomeasure soil nitrogen remotely of vehicle 10. Sensor(s) 140 may includehardware and/or software components. For example, sensor(s) 140 mayinclude a GPS receiver configured to receive positional data and asoftware component configured to determine positional parametersassociated with vehicle 10 (e.g., pose, speed, yaw, trajectory, etc.)based on the positional data. As another example, sensor(s) 140 mayinclude an optical device (e.g., a camera, LIDAR sensor, etc.)configured to capture image data and a software component configured toclassify obstacles based on the image data.

Communication system 150 may facilitate communication between vehicle 10and/or vehicle control system 110 and external systems. Communicationsystem 150 may be or include wired or wireless communications interfaces(e.g., jacks, antennas, transmitters, receivers, transceivers, wireterminals, etc.) for conducting data communications within controlenvironment 100 and/or with other external systems or devices. Invarious embodiments, communications via communication system 150 isdirect (e.g., local wired or wireless communications). Additionally oralternatively, communications via communication system 150 may utilize anetwork (e.g., a WAN, the Internet, a cellular network, avehicle-to-vehicle network, etc.). For example, vehicle control system110 may communicate with a decision support system (DSS) using a 4Gand/or 5G connection (e.g., via a 4G or 5G access point/small cell basestation, etc.) and may communicate with another vehicle using adedicated short-range communication channel (e.g., a vehicular ad-hocnetwork, etc.). In some embodiments, communication system 150facilitates vehicle-to-vehicle (V2V) and/or vehicle-to-everything (V2X)communication. For example, communication system 150 may facilitatecommunication between vehicle 10 and another vehicle using the IEEE802.11p standard (e.g., a wireless access in vehicular environments(WAVE) vehicular communication system). In some embodiments, vehicle 10communicates with external systems via Wi-Fi.

Referring now generally to vehicle control system 110, vehicle controlsystem 110 offers many benefits over existing systems. Conventionalvehicle parameter estimation systems typically require an array ofsensors (e.g., to provide several sources of input data) to generatevehicle parameter estimations. For example, a conventional vehicleparameter estimation system may require an array of torque sensorsdistributed throughout a transmission and engine of a vehicle tofacilitate parameter estimation. Such sensor array may be expensive.Furthermore, such sensors are difficult to retro-fit onto an existingvehicle, thereby limiting the application of conventional vehicleparameter estimation systems. Additionally, conventional vehicleparameter estimation systems typically require user calibration. Forexample, a conventional vehicle parameter estimation system may requirea user to provide a tire type (e.g., what type/types of tire(s) arebeing used with the vehicle, etc.) and/or a database describing thecharacteristics of various tire types in different conditions (e.g., drysoil vs. wet soil, etc.). Requiring user calibration may be inconvenientto users. Furthermore, if a user forgets to properly calibrate aconventional system (e.g., the user changes a tire type on the vehiclewithout updating the system, etc.), then the system may produce falseoutputs. However, vehicle control system 110 described hereinfacilitates real time vehicle parameter estimation without the expensivesensor arrays or user calibration associated with conventional systems.That is, in various embodiments, vehicle control system 110 maydetermine vehicle parameters using positional data from a GPStransceiver. For example, vehicle control system 110 may receivegeospatial data from a GPS receiver and use the geospatial data andknown vehicle parameters (e.g., vehicle mass, vehicle size, etc.) todetermine one or more parameters associated with interactions betweenthe vehicle tires and the ground. In various embodiments, vehiclecontrol system 110 eliminates the need for expensive sensor arraysassociated with conventional systems. Therefore, vehicle control system110 may facilitate retro-fitting existing vehicles and/or may facilitateless expensive vehicle parameter estimation than conventional systems.Furthermore, vehicle control system 110 may eliminate the need forcontinuous user calibration (e.g., updating a tire type every time thevehicle tires are changed, etc.) associated with conventional systems.Additionally, vehicle control system 110 may eliminate the need fordatabases describing the characteristics of various tire types indifferent conditions as associated with conventional systems.

Referring still to FIG. 1 , vehicle control system 110 is shown toinclude processing circuit 160 having processor 162 and memory 164. Insome embodiments, vehicle control system 110 includes one or moreprocessing circuits 160 including one or more processors 162 and one ormore memories 164. Each of processors 162 can be a general purpose orspecific purpose processor, an application specific integrated circuit(ASIC), one or more field programmable gate arrays (FPGAs), a group ofprocessing components, or other suitable processing components. Each ofprocessors 162 is configured to execute computer code or instructionsstored in memory 164 or received from other computer readable media(e.g., CDROM, network storage, a remote server, etc.).

Memory 164 may include one or more devices (e.g., memory units, memorydevices, storage devices, or other computer-readable media) for storingdata and/or computer code for completing and/or facilitating the variousprocesses described in the present disclosure. Memory 164 may includerandom access memory (RAM), read-only memory (ROM), hard drive storage,temporary storage, non-volatile memory, flash memory, optical memory, orany other suitable memory for storing software objects and/or computerinstructions. Memory 164 may include database components, object codecomponents, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present disclosure. Memory 164 may becommunicably connected to processor(s) 162 via processing circuit 160and may include computer code for executing (e.g., by processor 162) oneor more of the processes described herein.

Memory 164 is shown to include input circuit 170, estimation circuit180, and controller circuit 190. Input circuit 170 may facilitatereceiving and processing of information from external sources. Forexample, input circuit 170 may receive position and/or orientation datafrom sensor(s) 140. In various embodiments, input circuit 170 mayanalyze input data to determine one or more parameters. For example,input circuit 170 may receive geospatial data from sensor(s) 140 over aperiod of time and determine, based on the geospatial data, one or moremotion parameters associated with the vehicle (e.g., yaw rate, velocity,acceleration, heading, etc.). In some embodiments, input circuit 170facilitates integrating vehicle control system 110 with other systems.For example, input circuit 170 may geospatial data from a GPS receiverand may format the geospatial data for estimation circuit 180.

Estimation circuit 180 may facilitate determining vehicle parameters. Invarious embodiments, estimation circuit 180 receives data from inputcircuit 170 and uses the received data to estimate vehicle parameters(e.g., slip angles, tire stiffness, etc.). In various embodiments,estimation circuit 180 implements a feedback control system as describedin detail below with reference to FIGS. 3-4B. In various embodiments,estimation circuit 180 receives positional data from input circuit 170and/or sensor(s) 140. For example, estimation circuit 180 may receivegeospatial data from a GPS receiver. Additionally or alternatively,estimation circuit 180 may use known vehicle characteristics todetermine vehicle parameters. For example, estimation circuit 180 mayuse a mass and/or a size of vehicle 10 to determine slip anglesassociated with vehicle 10. In some embodiments, estimation circuit 180receives vehicle characteristics (e.g., mass, size, etc.) from sensor(s)140. For example, sensor(s) 140 may measure a loaded mass of vehicle 10(e.g., a mass of vehicle 10 when vehicle 10 is loaded with cargo, etc.)and send the loaded mass to estimation circuit 180.

Controller circuit 190 may facilitate control of vehicle 10. Forexample, controller circuit 190 may receive a slip angle from estimationcircuit 180 and generate control signals for primary mover 130 tooperate vehicle 10. As another example, controller circuit 190 mayreceive a slip angle from estimation circuit 180 and adjust a steeringangle based on the slip angle. In some embodiments, controller circuit190 interfaces with other systems. For example, controller circuit 190may receive a vehicle parameter (e.g., slip angles, tire stiffness,etc.) from estimation circuit 180 and interface with an anti-lockbraking system (ABS) and/or a traction control system (TCS). In someembodiments, controller circuit 190 facilitates autonomous control ofvehicle 10. For example, controller circuit 190 may adjust autonomoussteering control signals for vehicle 10 based on a received tirestiffness. Additionally or alternatively, controller circuit 190 maytransmit information to HMI 120 for display to a user. For example,controller circuit 190 may cause HMI 120 to display a safety warning toa user in response to a slip angle determined by estimation circuit 180exceeding a threshold.

Referring now to FIG. 2 , a diagram illustrating process 200 forestimating a vehicle parameter is shown, according to an exemplaryembodiment. Process 200 may be implemented by vehicle control system110. In brief summary, process 200 includes predicting vehicle motionbased on vehicle motion characteristics (e.g., speed, heading, etc.),measuring a position of the vehicle, comparing the predicted vehiclemotion to the measured position of the vehicle, and generating acorrection factor (e.g., a vehicle parameter) based on the comparison.Process 200 is described in relation to vehicle 10. In variousembodiments, vehicle control system 110 performs process 200continuously (e.g., in real time, etc.). Additionally or alternatively,vehicle control system 200 may perform process 200 periodically (e.g.,every five minutes, upon receiving new input data, etc.).

In various embodiments, vehicle 10 starts at first position 210. In someembodiments, first position 210 is associated with a first time. At step240, vehicle control system 110 may predict the motion of vehicle 10. Invarious embodiments, step 240 includes measuring motion characteristicsassociated with vehicle 10. For example, estimation circuit 180 mayreceive time-correlated geospatial data, such as a time series ofgeospatial data points, from sensor(s) 140 and determine a yaw rate,heading, velocity and/or other motion characteristics associated withvehicle 10. In various embodiments, the motion characteristics areassociated with vehicle 10 at first position 210 and/or the first time.For example, vehicle control system 110 may determine a yaw rate,heading, velocity, and acceleration associated with vehicle 10 at firstposition 210 and first time and use the determined values to predict themotion of vehicle 10. In various embodiments, predicting the motion ofvehicle 10 includes determining a future position and/or orientation ofvehicle 10. For example, vehicle control system 110 may determine apredicted geospatial location and/or vehicle characteristics (e.g., yawrate, heading, velocity, etc.) for vehicle 10 at a second time. Invarious embodiments, step 240 includes determining predicted position220. In various embodiments, predicted position 220 is associated with asecond time. In some embodiments, the second time is after the firsttime associated with first position 210.

At step 250, vehicle control system 110 measures a position of vehicle10. In various embodiments, step 250 includes determining secondposition 230. In various embodiments, step 250 is performed at thesecond time. For example, vehicle control system 110 may receivegeospatial data from sensor(s) 140 to determine a location of vehicle 10at the second time. In some embodiments, step 250 includes determiningvehicle motion characteristics (e.g., yaw rate, heading, velocity, etc.)associated with vehicle 10 at the second time.

At step 260, vehicle control system 110 compares predicted position 220to second position 230. In various embodiments, step 260 includesperforming an operation using predicted position 220 and second position230. For example, vehicle control system 110 may determine a differencebetween a yaw rate associated with predicted position 220 and a yaw rateassociated with second position 230. Additionally or alternatively,vehicle control system 110 may determine a ratio between a velocityassociated with predicted position 220 and a velocity associated withsecond position 230. In some embodiments, vehicle control system 110determine a physical offset between the location of predicted position220 and the location of second position 230. For example, vehiclecontrol system 110 may determine that second position 230 is ten feetnorth and five feet south of predicted position 220.

At step 270, vehicle control system 110 may generate a correctionfactor. In various embodiments, vehicle control system 110 generates thecorrection factor based on the comparison between predicted position 220and second position 230. In various embodiments, step 270 includesdetermining a vehicle parameter (e.g., a slip angle, tire stiffness,cornering stiffness, etc.) that accounts for a difference betweenpredicted position 220 and second position 230. As a non-limitingexample, vehicle control system 110 may determine that a differencebetween predicted position 220 and second position 230 can be attributedto an interaction between tires of vehicle 10 and the ground (e.g., wetsoil causing vehicle 10 to slip and veer off course, etc.). In variousembodiments, step 270 includes implementing a feedback control system asdescribed below in reference to FIGS. 3-4B.

Referring now to FIG. 3 , feedback control loop 300 for determining avehicle parameter is shown, according to an exemplary embodiment. Invarious embodiments, estimation circuit 180 implements feedback controlloop 300. Feedback control loop 300 is shown to include estimatedvehicle parameter(s) 310. Estimated vehicle parameter(s) 310 may includea slip angle, a tire stiffness, a cornering stiffness and/or the like.For example, estimated vehicle parameter(s) 310 may include previouslycomputed vehicle parameters (e.g., a slip angle, tire stiffness,cornering stiffness, etc.) fed back into feedback control loop 300.Additionally or alternatively, estimated vehicle parameter(s) 310 mayinclude position and/or orientation data. For example, estimated vehicleparameter(s) 310 may include geospatial data and/or motioncharacteristics (e.g., yaw rate, velocity, acceleration, heading, etc.).In some embodiments, estimated vehicle parameter(s) 310 include a singlevalue. Additionally or alternatively, estimated vehicle parameter(s) 310may include multiple values. Feedback control loop 300 may includemeasured vehicle position 320. In various embodiments, measured vehicleposition 320 includes geospatial data associated with vehicle 10.Additionally or alternatively, measured vehicle position 320 may includemotion characteristics (e.g., yaw rate, velocity, acceleration, heading,etc.) associated with vehicle 10. In some embodiments, sensor(s) 140provide measured vehicle position 320.

In various embodiments, control block 330 receives estimated vehicleparameter(s) 310 and/or measured vehicle position 320 and generatescorrection factor 340. Correction factor 340 may include a vehicleparameter. For example, correction factor 340 may include a tirestiffness associated with vehicle 10. In various embodiments, controlblock 330 represents a control law and/or control scheme used tocalculate a vehicle parameter (e.g., a tire stiffness, slip angle,etc.). In various embodiments, vehicle controls system 110 implementscontrol block 330. In some embodiments, control block 330 is or includesa linear controller. Additionally or alternatively, control block 330may be or include a non-linear controller. In some embodiments, controlblock 330 is implemented using machine-learning. For example, controlblock 330 may be or include a state estimation system (e.g., fuzzy logicsystem, neural network, linear/non-linear observers, Bayesian estimationsystem, etc.). In some embodiments, control block 330 implementsstatistical inference filtering. For example, control block 330 mayimplement a Kalman filter and/or a particle filter. In some embodiments,control block 330 is performed in real time. Additionally oralternatively, control block 330 may be performed using timings. Forexample, control block 330 may implement fixed lag, variable lag,adaptive lag, and/or the like. As a further example, vehicle controlsystem 110 may perform control block 330 on a fixed interval, a variableinterval, an adaptive interval, and/or the like.

Control block 330 is shown to include first block 332 and second block334. It should be understood that first block 332 and second block 334are meant to illustrate possible functions performed by control block330, however other functions are possible and within the scope of thepresent disclosure. First block 332 may predict the motion of vehicle10. For example, vehicle control system 110 may implement an algorithmto predict the motion of vehicle 10. In various embodiments, first block332 includes predicting a future position of vehicle 10 as describedabove with reference to FIG. 2 . Additionally or alternatively, firstblock 332 may include predicting one or more motion characteristics. Forexample, vehicle control system 110 may predict a yaw rate, a velocity,a heading, and a geospatial location of vehicle 10 at a second time. Insome embodiments, vehicle control system 110 models vehicle 10 using thefollowing equations:

$\overset{¨}{x} = {{\overset{.}{\psi}\overset{.}{y}} = a_{x}}$$\overset{¨}{y} = {{{- \overset{.}{\psi}}\overset{.}{x}} + {\frac{2}{m}( {{F_{c,f}\cos\mspace{14mu}\delta_{f}} + F_{c,r}} )}}$$\overset{¨}{\psi} = {\frac{2}{I_{z}}( {{l_{f}F_{c,f}} - {l_{r}F_{c,r}}} )}$$\overset{.}{X} = {{\overset{.}{x}\mspace{14mu}\cos\mspace{14mu}\psi} - {\overset{.}{y}\mspace{14mu}\sin\mspace{14mu}\psi}}$$\overset{.}{Y} = {{\overset{.}{x}\mspace{14mu}\sin\mspace{14mu}\psi} + {\overset{.}{y}\mspace{14mu}\cos\mspace{14mu}\psi}}$where {umlaut over (x)} is a longitudinal acceleration in a body frame,ÿ is a lateral acceleration in the body frame, {umlaut over (ψ)} is ayaw acceleration, {dot over (X)} is a longitudinal velocity of aninertial frame, {dot over (Y)} is a lateral velocity of the inertialframe, {dot over (ψ)} is a yaw rate, {dot over (y)} is a lateral speedin the body frame, a_(x) is a longitudinal acceleration of a center ofmass of the body within the inertial frame, m is the vehicle mass,F_(c,f) is a lateral tire force at the front wheels, δ_(f) is a frontsteering angle F_(c,r), is a lateral tire force at the rear wheels,I_(z) is a yaw inertia, l_(f) is a distance from the center of mass ofthe body to the front axle, l_(r) is a distance from the center of massof the body to the rear axle, {dot over (x)} is a longitudinal speed inthe body frame, and is an inertial heading.

Second block 334 may compare the predicted vehicle motion and themeasured vehicle motion. For example, second block 334 may compare theoutput of first block 332 and measured vehicle position 320. In variousembodiments, second block 334 includes comparing motion characteristics.For example, second block 334 may include comparing ψ_(m) and ψ_(p)where ψ_(m) is a measured inertial heading and ψ_(p) is a predictedinertial heading. Additionally or alternatively, second block 334 mayinclude comparing {dot over (ψ)}_(m) and {dot over (ψ)}_(p) where {dotover (ψ)}_(m) is a measured yaw rate and {dot over (ψ)}_(p) is apredicted yaw rate. In various embodiments, the output of second block334 is correction factor 340. In some embodiments, correction factor 340includes a vehicle parameter. For example, correction factor 340 mayinclude a slip angle associated with a difference between ψ_(m) andψ_(p). Additionally or alternatively, the vehicle parameter may begenerated based on correction factor 340. For example, vehicle controlsystem 110 may use correction factor 340 to calculate a slip angleassociated with vehicle 10.

Referring now to FIGS. 4A-4B, a block diagram of a control system 400that may be implemented by vehicle control system 110 is shown,according to an exemplary embodiment. In various embodiments, controlsystem 400 receives inputs 402. In some embodiments, inputs 402 includesteering angle δ, yaw rate {dot over (ψ)}, inertial heading ψ, andlateral vehicle speed v_(y). Additionally or alternatively, inputs 402may include output(s) 462. For example, output(s) 462 may be fed backinto control system 400. In some embodiments, control system 400includes other inputs. For example, vehicle control system 110 mayutilize vehicle characteristics such as a wheelbase of vehicle 10, amass of vehicle 10, an inertia of vehicle 10, a center of mass ofvehicle 10, and/or the like. In some embodiments, control system 400utilizes motion characteristics received from sensor(s) 140. Forexample, sensor(s) 140 may measure a heading of vehicle 10, a headingrate of vehicle 10, a north/east velocity of vehicle 10, a lateralvelocity of vehicle 10, and/or a steering angle of vehicle 10.

In various embodiments, control system 400 includes yaw rate calculator412, yaw calculator 414, global lateral velocity calculator 416, locallateral velocity calculator 418, comparators 422-428, gain weightedsummer 432 and 434, and correction factor calculator 450. In someembodiments, control system 400 includes a different number and/orarrangement of components. Yaw rate calculator 412 may calculate a yawrate {dot over (ψ)}. In some embodiments, yaw rate calculator 412implements the following equation:

$\overset{.}{\psi} = {\int{\frac{2}{I_{z}}( {{C_{f}\delta\; l_{f}\mspace{14mu}{\cos(\delta)}} - {C_{f}l_{f}\mspace{14mu}{\cos(\delta)}\alpha_{f}} + {C_{r}l_{r}\alpha_{r}}} )}}$where C_(f) is a cornering stiffness of a front tire, α_(f) is a tireslip angle of a front tire, C_(r) is a cornering stiffness of a reartire, and α_(r) is a tire slip angle of a rear tire.

Yaw calculator 414 may calculate an inertial heading ψ. In someembodiments, yaw calculator 414 implements the following equation:ψ=∫{dot over (ψ)}

Global lateral velocity calculator 416 may calculate a lateral speed inthe body frame {dot over (y)}. In some embodiments, global lateralvelocity calculator 416 implements the following equation:

$\overset{.}{y} = {\int{\frac{1}{m}( {{2\; C_{f}\;\delta\mspace{14mu}{\cos(\delta)}} - {2\; C_{f}\mspace{14mu}{\cos(\delta)}\alpha_{f}} - {2\; C_{r}\alpha_{r}} - {v_{x}m\;\overset{.}{\theta}}} )}}$where v_(x) is a longitudinal vehicle speed, and {dot over (θ)} is anangular velocity of the center of mass of the vehicle with respect to alongitudinal axis of the vehicle.

Local lateral velocity calculator 418 may calculate a lateral vehiclespeed v_(y). In some embodiments, local lateral velocity calculator 418implements the following equation:

$v_{y} = {V\;{\sin( {{\sin^{- 1}( \frac{v_{x}}{V} )} - {\sin^{- 1}( \frac{\overset{.}{x}}{V} )} + {\sin^{- 1}( \frac{\overset{.}{y}}{V} )}} )}}$where V=√{square root over (v_(x) ²+v_(y) ²)}.

Comparators 422-428 may perform a comparison operation on inputs toproduce an output. For example, comparator 422 may implement output={dotover (ψ)}_(m)−{dot over (ψ)}_(p). In various embodiments, the outputs ofyaw rate calculator 412, yaw calculator 414, global lateral velocitycalculator 416, and local lateral velocity calculator 418 are predictedparameters. In various embodiments, comparators 422-428 determine adifference. Additionally or alternatively, comparators 422-428 mayimplement a different operation (e.g., determine a ratio, a weightedsum, etc.).

Gain weighted summer 432 and 434 may perform a weighted summationoperation on inputs to produce an output. For example, gain weightedsummer 432 may implement output=K₁{dot over (ψ)}+K₂ψ where K₁ and K₂ aregain values. In some embodiments, K₁ and K₂ are user configurable (e.g.,determined by a user, determined as part of a configuration process,etc.). Additionally or alternatively, K₁ and K₂ may be determined byvehicle control system 110. For example, vehicle control system 110 mayadjust K₁ and K₂ based on operational data to determine a value for K₁and K₂ that gives the most accurate output(s) 462. As a further example,K₁ and K₂ may be determined using a membership selection function. Invarious embodiments, gain weighted summer 432 and 434 receive inputsfrom comparators 422-428. For example, gain weighted summer 432 mayreceive the output of comparator 422 and 424. In various embodiments,gain weighted summer 432 and 434 perform an addition operation.Additionally or alternatively, gain weighted summer 432 and 434 mayimplement a different operation (e.g., determine a ratio, a difference,etc.).

Correction factor calculator 450 may generate a correction factor. Invarious embodiments, the correction factor is a vehicle parameter. Forexample, the correction factor may include a slip angle. In variousembodiments, correction factor calculator 450 produces output(s) 462.Output(s) 462 may include a tire cornering stiffness. For example,output(s) 462 may include C_(f) and C_(r). Additionally oralternatively, output(s) 462 may include a cornering stiffnessassociated with each tire of vehicle 10 (e.g., if vehicle 10 has sixtires installed, output(s) 462 may include a cornering stiffnessassociated with each of the six tires). In various embodiments,correction factor calculator 450 determines output(s) 462 by adjustingone or more values associated with output(s) 462 to minimize an errorassociated with a predicted position of vehicle 10 and a measuredposition of vehicle 10 at the second time, as described above inreference to FIG. 2 . For example, correction factor calculator 450 maydetermine a value for C_(f) and C_(r) that accounts for a difference inthe measured position of vehicle 10 and the predicted position ofvehicle 10 (e.g., a determined tire cornering stiffness and/or slipangle caused vehicle 10 to deviate from the predicted position by theobserved amount, etc.). In some embodiments, correction factorcalculator 450 implements one or more algorithms. For example,correction factor calculator 450 may implement a first algorithm todetermine C_(f) and a second algorithm to determine C_(r). Additionallyor alternatively, correction factor calculator 450 may be or include amachine-learning element. For example, correction factor calculator 450may include a neural network trained using a dataset of vehicle motioncorresponding to different vehicle parameters (e.g., slip conditions,tire stiffnesses, etc.) and configured to determine output(s) 462 giventhe output of gain weighted summer 432 and 434.

Referring now specifically to FIG. 4B, control system 400 may includefuzzy logic elements 442 and 444. Fuzzy logic elements 442 and 444 mayfacilitate updating output(s) 462 based on a confidence in the measuredmotion characteristics (e.g., {dot over (ψ)}m, ψ_(m), v_(y,m), {dot over(y)}_(m), etc.). For example, fuzzy logic elements 442 and 444 mayfacilitate rejecting noisy values of {dot over (ψ)}_(m). In variousembodiments, fuzzy logic elements 442 and 444 implement a fuzzy logicalgorithm that analyzes a numerical stability of output(s) 462 and aconfidence associated with the measured motion characteristics todetermine a confidence in output(s) 462. In various embodiments, fuzzylogic elements 442 and 444 facilitate real time determination ofoutput(s) 462. For example, vehicle control system 110 may continuouslydetermine a cornering stiffness associated with tires of vehicle 10 anddisplay the determined cornering stiffness to an operator in real time.

Referring now to FIG. 5 , method 500 of determining a vehicle parameteris shown, according to an exemplary embodiment. In various embodiments,vehicle control system 110 implements method 500. In variousembodiments, method 500 is used to determine C_(f) and/or C_(r).Additionally or alternatively, method 500 may determine other vehicleparameters (e.g., slip angles, tire stiffness, etc.). In someembodiments, method 500 may be used to calibrate vehicle 10 and/orvehicle control system 110. For example, method 500 may be performed ona surface having known characteristics (e.g., a paved surface, a road,etc.) to determine tire parameters associated with tires of vehicle 10,and thereby determine a type of tire installed on vehicle 10. As afurther example, upon detecting a change in tires, vehicle controlsystem 110 may perform method 500 to determine a type of tire installedon vehicle 10 and save the tire type for future use. Additionally oralternatively, method 500 may be performed in real time to facilitatevehicle operation. For example, vehicle control system 110 maycontinuously implement method 500 to update a cornering stiffnessassociated with different environmental conditions experienced byvehicle 10 and use the cornering stiffness to augment a TCS.

At step 510, vehicle control system 110 may receive vehicle positionmeasurements. For example, vehicle control system 110 may receivegeospatial data from sensor(s) 140. In various embodiments, step 510includes receiving multiple position measurements. For example, vehiclecontrol system 110 may receive timeseries geospatial data (e.g., firstgeospatial data associated with a first time, second geospatial dataassociated with a second time, etc.) from sensor(s) 140. In someembodiments, step 510 includes computing one or more motioncharacteristics (e.g., a heading, a velocity, a yaw rate, anacceleration, etc.) based on the position measurements. Additionally oralternatively, vehicle control system 110 may receive the one or moremotion characteristics from sensor(s) 140.

At step 520, vehicle control system 110 may predict a position ofvehicle 10. In various embodiments, vehicle control system 110 analyzesthe position measurements to predict a future position of vehicle 10, asdescribed in detail above with reference to FIGS. 2-4B. For example,vehicle control system 110 may determine a location and/or a headingassociated with vehicle 10 at a future time. In various embodiments,step 520 includes determining one or more motion characteristics. Invarious embodiments, vehicle control system 110 performs step 520 usingknown vehicle parameters (e.g., vehicle mass, vehicle wheel base, etc.),estimated parameters (e.g., a previously computed cornering stiffness,etc.), and/or other information (e.g., vehicle position measurements,etc.).

At step 530, vehicle control system 110 may compare the predictedvehicle position to the measured vehicle position. In variousembodiments, step 530 includes comparing predicted motioncharacteristics to measured motion characteristics as described indetail with reference to FIGS. 4A-4B above. For example, step 530 mayinclude comparing {dot over (ψ)}_(m) and {dot over (ψ)}_(p). In someembodiments, step 530 includes comparing a prediction location ofvehicle 10 to a measured location of vehicle 10. For example, vehiclecontrol system 110 may predict, at a first point in time, a location ofvehicle 10 associated with a second point in time, and may compare themeasured location of vehicle 10 at the second point in time to thepredicted location of vehicle 10 at the second point in time.

At step 540, vehicle control system 110 may generate a correctionfactor. In various embodiments, the correction factor includes a vehicleparameter. For example, the correction factor may include a corneringstiffness associated with a tire of vehicle 10. In various embodiments,vehicle control system 110 generates the correction factor based on thedifference between the predicted position of vehicle 10 and the measuredposition of vehicle 10 as described in detail above with reference toFIGS. 4A-4B. In various embodiments, step 540 includes updating anexisting vehicle parameter. For example, vehicle control system 110 mayupdate an estimated value of C_(f) and/or C_(r).

At step 550, vehicle control system 110 may generate one or moreestimated vehicle parameters. For example, step 550 may includedetermining a value of C_(f) and/or C_(r). Additionally oralternatively, step 550 may include determining other vehicleparameters. For example, step 550 may include determining a tire type.In various embodiments, vehicle control system 110 determines the one ormore estimated vehicle parameters based on the correction factorgenerated in step 540. For example, step 550 may include adjusting aslip angle to account for a difference between a predicted position ofvehicle 10 and a measured position of vehicle 10 and determining the oneor more estimated vehicle parameters based on the adjusted slip angle.In some embodiments, step 550 is optional. As a non-limiting example,vehicle control system 110 may additionally or alternatively determinewhen the correction factor and/or estimated vehicle parameter(s) are outof a threshold range and in response may transmit a signal. As anadditional non-limiting example, vehicle control system 110 mayadditionally or alternatively display the correction factor to a user(e.g., via HMI 120, etc.).

At step 560, vehicle control system 110 may use the one or moreestimated vehicle parameters to control vehicle 10. For example, vehiclecontrol system 110 may use C_(f) and/or C_(r) to control a TCS. As afurther example, vehicle control system 110 may use a slip angle toadjust the operation of an autonomous steering controller to ensurevehicle 10 stays on route. Additionally or alternatively, vehiclecontrol system 110 may transmit the one or more estimated vehicleparameters to another system to facilitate further functionality. Forexample, vehicle control system 110 may transmit a slip angle to HMI 120to display a real time safety warning to a user if the slip angleexceeds a threshold. In some embodiments, step 560 is optional.

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements can bereversed or otherwise varied and the nature or number of discreteelements or positions can be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepscan be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions can be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure can be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps canbe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

The term “client or “server” include all kinds of apparatus, devices,and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing. The apparatus may includespecial purpose logic circuitry, e.g., a field programmable gate array(FPGA) or an application specific integrated circuit (ASIC). Theapparatus may also include, in addition to hardware, code that createsan execution environment for the computer program in question (e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more ofthem). The apparatus and execution environment may realize variousdifferent computing model infrastructures, such as web services,distributed computing and grid computing infrastructures.

The systems and methods of the present disclosure may be completed byany computer program. A computer program (also known as a program,software, software application, script, or code) may be written in anyform of programming language, including compiled or interpretedlanguages, declarative or procedural languages, and it may be deployedin any form, including as a stand-alone program or as a module,component, subroutine, object, or other unit suitable for use in acomputing environment. A computer program may, but need not, correspondto a file in a file system. A program may be stored in a portion of afile that holds other programs or data (e.g., one or more scripts storedin a markup language document), in a single file dedicated to theprogram in question, or in multiple coordinated files (e.g., files thatstore one or more modules, sub programs, or portions of code). Acomputer program may be deployed to be executed on one computer or onmultiple computers that are located at one site or distributed acrossmultiple sites and interconnected by a communication network.

The processes and logic flows described in this specification may beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows may also be performedby, and apparatus may also be implemented as, special purpose logiccircuitry (e.g., an FPGA or an ASIC).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data (e.g., magnetic, magneto-optical disks, or optical disks).However, a computer need not have such devices. Moreover, a computer maybe embedded in another device (e.g., a vehicle, a Global PositioningSystem (GPS) receiver, etc.). Devices suitable for storing computerprogram instructions and data include all forms of non-volatile memory,media and memory devices, including by way of example semiconductormemory devices (e.g., EPROM, EEPROM, and flash memory devices; magneticdisks, e.g., internal hard disks or removable disks; magneto-opticaldisks; and CD ROM and DVD-ROM disks). The processor and the memory maybe supplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this specification may be implemented on a computerhaving a display device (e.g., a CRT (cathode ray tube), LCD (liquidcrystal display), OLED (organic light emitting diode), TFT (thin-filmtransistor), or other flexible configuration, or any other monitor fordisplaying information to the user. Other kinds of devices may be usedto provide for interaction with a user as well; for example, feedbackprovided to the user may be any form of sensory feedback (e.g., visualfeedback, auditory feedback, or tactile feedback).

Implementations of the subject matter described in this disclosure maybe implemented in a computing system that includes a back-end component(e.g., as a data server), or that includes a middleware component (e.g.,an application server), or that includes a front end component (e.g., aclient computer) having a graphical user interface or a web browserthrough which a user may interact with an implementation of the subjectmatter described in this disclosure, or any combination of one or moresuch back end, middleware, or front end components. The components ofthe system may be interconnected by any form or medium of digital datacommunication (e.g., a communication network). Examples of communicationnetworks include a LAN and a WAN, an inter-network (e.g., the Internet),and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The present disclosure may be embodied in various different forms, andshould not be construed as being limited to only the illustratedembodiments herein. Rather, these embodiments are provided as examplesso that this disclosure will be thorough and complete, and will fullyconvey the aspects and features of the present disclosure to thoseskilled in the art. Accordingly, processes, elements, and techniquesthat are not necessary to those having ordinary skill in the art for acomplete understanding of the aspects and features of the presentdisclosure may not be described. Unless otherwise noted, like referencenumerals denote like elements throughout the attached drawings and thewritten description, and thus, descriptions thereof may not be repeated.Further, features or aspects within each example embodiment shouldtypically be considered as available for other similar features oraspects in other example embodiments.

It will be understood that, although the terms “first,” “second,”“third,” etc., may be used herein to describe various elements,components, regions, layers and/or sections, these elements, components,regions, layers and/or sections should not be limited by these terms.These terms are used to distinguish one element, component, region,layer or section from another element, component, region, layer orsection. Thus, a first element, component, region, layer or sectiondescribed below could be termed a second element, component, region,layer or section, without departing from the spirit and scope of thepresent disclosure.

The terminology used herein is for the purpose of describing particularembodiments and is not intended to be limiting of the presentdisclosure. As used herein, the singular forms “a” and “an” are intendedto include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises,” “comprising,” “includes,” and “including,” “has,” “have,”and “having,” when used in this specification, specify the presence ofthe stated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof. As used herein, the term “and/or” includes anyand all combinations of one or more of the associated listed items.Expressions such as “at least one of,” when preceding a list ofelements, modify the entire list of elements and do not modify theindividual elements of the list.

As used herein, the term “substantially,” “about,” and similar terms areused as terms of approximation and not as terms of degree, and areintended to account for the inherent variations in measured orcalculated values that would be recognized by those of ordinary skill inthe art. Further, the use of “may” when describing embodiments of thepresent disclosure refers to “one or more embodiments of the presentdisclosure.” As used herein, the terms “use,” “using,” and “used” may beconsidered synonymous with the terms “utilize,” “utilizing,” and“utilized,” respectively. Also, the term “exemplary” is intended torefer to an example or illustration.

What is claimed is:
 1. A system for estimating tire parameters for anoff-road vehicle in real time, the system comprising: a processingcircuit including a processor and memory, the memory having instructionsstored thereon that, when executed by the processor, cause theprocessing circuit to: measure a position of the vehicle at a firsttime; determine, based on the position, motion characteristics of thevehicle; predict, based on the motion characteristics, a position of thevehicle at a second time; measure a position of the vehicle at thesecond time; determine a correction factor based on the differencebetween the predicted position and the measured position of the vehicleat the second time; and determine a tire parameter associated with thevehicle based on the correction factor, wherein the correction factorincludes at least one of a slip angle, a tire stiffness, or a corneringstiffness.
 2. The system of claim 1, wherein the tire parameter is acornering stiffness.
 3. The system of claim 1, wherein the tireparameter is a tire type.
 4. The system of claim 1, wherein thecorrection factor is associated with an amount of tire slip associatedwith the difference between the predicted position and the measuredposition of the vehicle at the second time.
 5. The system of claim 4,wherein determining the tire parameter includes adjusting the correctionfactor to account for the difference between the predicted position andthe measured position of the vehicle at the second time, wherein theadjusted correction factor is the tire parameter.
 6. The system of claim4, wherein the difference between the predicted position and themeasured position of the vehicle at the second time includes two or moreparameters associated with the vehicle position and wherein the methodincludes weighting each of the two or more parameters based on acontribution each of the two or more parameters make to the differencebetween the predicted position and the measured position of the vehicleat the second time.
 7. The system of claim 1, wherein the vehicle is anagricultural vehicle.
 8. The system of claim 1, wherein measuring theposition of the vehicle at the first and second times includes receivingposition information from a GPS receiver associated with the vehicle. 9.The system of claim 1, wherein the tire parameter is determined furtherbased on vehicle characteristics associated with the vehicle.
 10. Thesystem of claim 1, wherein the processing circuit is further configuredto control an operation of the vehicle based on the tire parameter. 11.The system of claim 1, wherein correction factor is determined based onthe comparison of a yaw rate, the inertial heading, the lateral speed ofthe body frame, and the lateral speed of the off-road vehicle.
 12. Amethod of estimating tire parameters for an off-road vehicle in realtime, the method comprising: measuring a position of the vehicle at afirst time; determining, based on the position, motion characteristicsassociated with the vehicle; predicting, based on the motioncharacteristics, a position of the vehicle at a second time; measuring aposition of the vehicle at the second time; determining a correctionfactor based on the difference between the predicted position and themeasured position of the vehicle at the second time; and determining atire parameter associated with the vehicle based on the correctionfactor, wherein the correction factor includes at least one of a slipangle, a tire stiffness, or a cornering stiffness.
 13. The method ofclaim 12, wherein the tire parameter is a cornering stiffness.
 14. Themethod of claim 12, wherein the tire parameter is a tire type.
 15. Themethod of claim 12, wherein the correction factor is associated with anamount of tire slip associated with the difference between the predictedposition and the measured position of the vehicle at the second time.16. The method of claim 15, wherein determining the tire parameterincludes adjusting the correction factor to account for the differencebetween the predicted position and the measured position of the vehicleat the second time, wherein the adjusted correction factor is the tireparameter.
 17. The method of claim 15, wherein the difference betweenthe predicted position and the measured position of the vehicle at thesecond time includes two or more parameters associated with the vehicleposition and wherein the method includes weighting each of the two ormore parameters based on a contribution each of the two or moreparameters make to the difference between the predicted position and themeasured position of the vehicle at the second time.
 18. The method ofclaim 12, wherein the vehicle is an agricultural vehicle.
 19. The methodof claim 12, wherein correction factor is determined based on thecomparison of a yaw rate, the inertial heading, the lateral speed of thebody frame, and the lateral speed of the off-road vehicle.
 20. Anagricultural vehicle having one or more tires and a vehicle controlsystem including a processor and memory, the memory having instructionsstored thereon that, when executed by the processor, cause the processorto: receive a position measurement associated with the agriculturalvehicle at a first time; determine, based on the position, motioncharacteristics associated with the agricultural vehicle; generate,based on the motion characteristics, a predicted position of theagricultural vehicle at a second time; measure a position of theagricultural vehicle at the second time; determine a correction factorbased on the difference between the predicted position and the measuredposition of the vehicle at the second time; and determine a corneringstiffness associated with at least one of the one or more tires based onthe correction factor, wherein the correction factor includes at leastone of a slip angle, a tire stiffness, or a cornering stiffness.